Combined Analysis to Characterize Yield Pattern of Greenhouse-grown Red Sweet Peppers
Bibliographic record
Abstract
Understanding the irregular yield pattern of greenhouse-grown sweet peppers ( Capsicum annuum L.) has been a challenge to researchers and greenhouse producers. Experimental data from 4 years, each consisting of 26 production weeks, were used in a time series analysis, neural network (NN) modeling, and regression analysis. Time series analysis revealed that weekly yield was influenced by yields from the preceding 2 weeks (Yd_1 and Yd_2), cumulative light 2 and 4 weeks prior (L_2 and L_4), and average 24-h air temperature 5 weeks prior (T_5). Cumulative light (L) data were transformed into kL by dividing by 1000 for subsequent NN modeling and regression analysis. These five inputs were used to establish a NN model, which illustrated the positive influence of Yd_1, kL_4, and kL_2 and negative influence of Yd_2 and T_5. Again, these five inputs were used in a regression analysis illustrating the positive influence of Yd_1 and the negative influence of Yd_2. Each input was further modified to include its squared value before entering the regression, which resulted in significant inputs of Yd_1, Yd_1 squared, and Yd_2 squared. Among these three analyses, the most consistent parameters were Yd_1 and Yd_2, confirming that the irregular yield pattern of greenhouse-grown peppers is of a biological nature. Environmental factors kL_2, kL_4, and T_5 did not show a consistent effect on yield in all three analyses, indicating yield pattern is less influenced by growing environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".